Ailed: A Psyche-Driven Chess Engine with Dynamic Emotional Modulation

TL;DR

The patterns are reminiscent of tilt and overconfidence as described in human play, but I should be upfront: this study includes no human-subject validation.

Abstract

Chess engines passed human strength years ago, but they still don't play like humans. A grandmaster under clock pressure blunders in ways a club player on a hot streak never would. Conventional engines capture none of this. This paper proposes a personality x psyche decomposition to produce behavioral variability in chess play, drawing on patterns observed in human games. Personality is static -- a preset that pins down the engine's character. Psyche is dynamic -- a bounded scalar ψ_t \in [-100, +100], recomputed from five positional factors after every move. These two components feed into an audio-inspired signal chain (noise gate, compressor/expander, five-band equalizer, saturation limiter) that reshapes move probability distributions on the fly. The chain doesn't care what engine sits behind it: any system that outputs move probabilities will do. It needs no search and carries no state beyond ψ_t. I test the framework across 12,414 games against Maia2-1100, feeding it two probability sources that differ by ~2,800x in training data. Both show the same monotonic gradient in top-move agreement (~20-25 pp spread from stress to overconfidence), which tells us the behavioral variation comes from the signal chain, not from the model underneath. When the psyche runs overconfident, the chain mostly gets out of the way (66% agreement with vanilla Maia2). Under stress, the competitive score falls from 50.8% to 30.1%. The patterns are reminiscent of tilt and overconfidence as described in human play, but I should be upfront: this study includes no human-subject validation.

Figures (8)

• Figure 1: Psyche computation pipeline. Five positional factors are extracted from the board, weighted, and summed to produce a raw evaluation $e_t$, which is tanh-compressed to a target psyche $\hat{\psi}_t \in [-100,+100]$.
• Figure 2: Signal chain parameters for the "human" personality preset as a function of psyche $\psi$. Gate threshold is scaled $\times 20$ for visibility. The dynamics power $\alpha$ crosses unity at $\psi = 0$: below neutral it acts as an expander (flattening the distribution), above as a compressor (sharpening it). All parameters interpolate linearly between the three psyche anchors.
• Figure 3: EQ band gains for the "human" personality preset at three psyche conditions, displayed as equalizer panels. Each dot is a band control point (Best, Good, Mild, Bad, Worst); the smooth interpolated curve and shaded fill show the gain deviation from the white unity baseline (above $=$ boost; below $=$ cut). Under stress ($\psi=-70$), humps at "good" and "bad" shift weight toward adjacent tiers. At neutral ($\psi=0$), a strong monotone preference ordering is imposed. At overconfidence ($\psi=+70$), all deviations converge toward zero and the EQ becomes nearly flat.
• Figure 4: Ailed system architecture. The move predictor $f_\theta$ produces logits fed into the move selector; the psyche calculator computes the psyche state $\psi_t$ from board position evaluations. Psyche state modulates all three output paths: move selection (signal chain), thinking mode (plan disruption), and study mode (skip probability and exploration quality). Dashed arrows indicate conditional paths.
• Figure 5: Thinking mode lifecycle. A plan is generated when confidence exceeds the threshold, stored in a single-slot buffer, and executed only if the opponent's actual move matches the prediction and the psyche disruption check passes.

Theorems & Definitions (5)

• Definition 1: Psyche update
• Definition 2: Daily decay
• Definition 3: Plan disruption
• Definition 4: Study skip probability
• Definition 5: Effective study duration